Exam Professional Machine Learning Engineer topic 1 question 123 discussion

Preserves Information: Deleting rows (Option A) throws away valuable data, especially since every instance is important. Not Applicable Technique: Feature crossing (Option B) creates new features by multiplying existing features. It wouldn't address missing values directly. Zero Imputation Might Bias: Replacing missing values with zeros (Option D) can introduce bias if zeros have a specific meaning in the data (e.g., distance cannot be zero).

Went with A: Predict the missing values using linear regression as the data does not have high variance.

Went with C

Answer is C Predicting the missing values using linear regression can be a good approach, especially if the variable is important for the prediction. The values can be imputed using regression, where the missing variable can be the dependent variable, and other relevant variables can be used as predictors

Regression https://cran.r-project.org/web/packages/miceRanger/vignettes/miceAlgorithm.html • Find linear or non-linear relationships between the missing feature and other features • Most advanced technique: MICE (Multiple Imputation by Chained Equations)

It's C.

My answer was based on the below article https://towardsdatascience.com/7-ways-to-handle-missing-values-in-machine-learning-1a6326adf79e

One of the ways to handle missing data is deleting the rows. but question here says that every row is important. so I think another possible option could be to predict the missing value. Option C could be correct !

C (not sure)

A no - Every row is important B no - product of other feature values with no values makes no sense to me D no - zero value would bias the model as zero distance from school has the highest value to model C yes - there is an approach using linear regression to predict missing values